做多模态医学AI的团队终于有了判断模型是否学到真实生物学的工具——DECAT能揪出被AUROC掩盖的虚假关联，建议做肿瘤多模态研究的开发者点开看看。
多模态肿瘤模型能做出准确预测，但无法判断其是否学到跨模态共享的生物学、单一模态的生物学，还是虚假相关性。研究者提出DECAT，一个模型无关的后验评估框架，通过五个零假设参考指标和规则决策，将多模态表征分为四种诊断场景。在合成数据（2500+训练表征）和真实TCGA数据（8979名患者）上验证，发现CLIP等纠缠模型在检测共享生物学上近乎完美，但在大多数不存在共享生物学的情况下错误声称存在，且错误率随混杂强度增加。DECAT无需知道具体混杂因素，就能检测出AUROC无法发现的混杂。
When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework
Multimodal models in oncology can produce accurate predictions, but accurate prediction does not reveal whether the model has learned biology that is shared across modalities, biology confined to one modality, or spurious correlations that reflect confounders rather than genuine biology. We introduce DECAT, a model-agnostic post-hoc evaluation framework that classifies multimodal representations into four diagnostic scenarios for a given task and modality, using five null-referenced metrics and a rule-based decision procedure. The framework operates on learned representations, requires no knowledge of which specific confounder is present, and returns indeterminate when the evidence is insufficient. We validate DECAT on synthetic data across four multimodal model classes (over 2,500 trained representations) and on real data from 8,979 TCGA patients, evaluating both multimodal embeddings and five pretrained pathology foundation models. Entangled models (e.g., CLIP) achieve near-perfect shared biology detection but falsely claim shared biology in the majority of cases where it is absent on real foundation model embeddings. This false claim rate increases with confound strength so that larger cohorts and stronger representations produce more confident but still incorrect diagnoses. Applied to both multimodal TCGA embeddings and five pathology foundation models without paired RNA, DECAT detects confounding invisible to AUROC without requiring the confounder labels, as confirmed by post-hoc stratification.